# 3 - 3 - 3 3层神经网络的实现
import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def identity_function(x):
    return x

def init_network():
    network = {}
    network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
    network['b1'] = np.array([0.1, 0.2, 0.3])
    network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
    network['b2'] = np.array([0.1, 0.2])
    network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])
    network['b3'] = np.array([0.1, 0.2])

    return network

def forward(network, x):
    W1, W2, W3 = network['W1'], network['W2'], network['W3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']

    a1 = np.dot(x, W1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1, W2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2, W3) + b3
    y = identity_function(a3)

    return y

def main():
    network = init_network()
    x = np.array([1.0, 0.5])
    y = forward(network, x)
    print(y)

if __name__ == '__main__':
    X = np.array([[1.0, 0.5]])
    W1 = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
    B1 = np.array([0.1, 0.2, 0.3])

    print(W1.shape)
    print(B1.shape)
    print(X.shape)

    # 输入层到第一层的信号传递
    A1 = np.dot(X, W1) + B1
    print(A1)
    Z1 = sigmoid(A1)
    print(Z1)
    # 第一层到第二层的信号传递
    W2 = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
    B2 = np.array([0.1, 0.2])

    A2 = np.dot(Z1, W2) + B2
    Z2 = sigmoid(A2)
    print(Z2)

    # 第二层到输出层的信号传递
    W3 = np.array([[0.1, 0.3], [0.2, 0.4]])
    B3 = np.array([0.1, 0.2])

    A3 = np.dot(Z2, W3) + B3
    Y = identity_function(A3)
    print(Y)

    main()